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Discrete Dynamics in Nature and Society

Volume 2014 (2014), Article ID 802429, 8 pages

http://dx.doi.org/10.1155/2014/802429

## Linear Control of Fractional-Order Financial Chaotic Systems with Input Saturation

^{1}School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China^{2}Department of Mathematics and Computational Sciences, Huainan Normal University, Huainan 232038, China

Received 12 March 2014; Accepted 17 July 2014; Published 6 August 2014

Academic Editor: Taishan Yi

Copyright © 2014 Junhai Luo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

In this paper, control of fractional-order financial chaotic systems with saturated control input is
investigated by means of state-feedback control method. The saturation problem is tackled by using
Gronwall-Bellman lemma and a memoryless nonlinearity function. Based on Gronwall inequality and
Laplace transform technique, two sufficient conditions are achieved for the asymptotical stability of the
fractional-order financial chaotic systems with fractional orders 0 < *α* ≤ 1 and 1 < *α* < 2, respectively.
Finally, simulation studies are carried out to show the effectiveness of the proposed linear control
method.

#### 1. Introduction

In the past two decades, studies of chaotic systems have received more and more attention in various fields of natural sciences. This is because chaotic systems are rich in dynamics and possess great sensitivity to initial conditions. Up to now, econophysics has been raised to an alternative scientific methodology to comprehend the highly complex dynamics in economic and financial systems. Many economists are working hard to explain the central features of economic data, including erratic macroeconomic fluctuations (business cycles), irregular microeconomic fluctuations, irregular growth, structural changes, and overlapping waves of economic development [1, 2]. Representative effects, that is, treated as random shocks, are political events, weather variables, and other human factors [3–7]. Compared with the opinion discussed above, chaos supports an endogenous explanation of the complexity appeared in economic series.

Since chaos in financial systems was firstly studied in 1985, great impact has been put on the prominent economics recently, because the occurrence of the chaotic phenomenon in the economic system indicates that the macroeconomic operation has in itself the inherent indefiniteness. Studies on the complicated financial systems by using nonlinear method are fruitful [2, 8, 9]. Controlling chaos in fractional-order financial systems is also studied in recent years [10–18]. In [15], an active sliding mode controller is constructed to synchronize fractional-order financial chaotic systems in master-slave structure. In [16], a necessary condition is introduced to confirm the existence of 1-scroll, 2-scroll, or multiscroll chaotic attractors in a fractional-order financial system and a sliding mode controller is proposed. Active control method is also used in [17], and the variable-order fractional derivative is defined in Caputo type. Wang et al. investigate impulsive synchronization and adaptive-impulsive synchronization of a novel financial hyperchaotic system [18]. In above literatures, the stability analysis is carried out based on fractional-order linear system stability theorem and only the situation where fractional order is concerned.

Most of real world technical systems are subjected to input constraints, especially in financial systems. In financial systems, input saturation does exist due to a limited size of weather variables, political events, and other human factors. The existence of input saturation may decrease the control performance or cause oscillations and even lead to instability of the system [19–21]. It is advisable for us to consider the control of financial systems with input saturation. For the integer-order linear and nonlinear systems, input saturation has received much attention from researchers in the past decade. The sector bounded condition associated with input nonlinearities is useful for analysis and synthesis of control systems subject to input saturation. Then the stability of the system can be formulated using Lyapunov stability theory and invariant theory.

Though many research efforts have been put to the fractional-order financial chaotic systems, the financial systems with saturated control input have rarely been investigated in literatures. Here, with the help of Laplace transform, Mittag-Leffler function, and Gronwall inequality, a linear controller will be derived for fractional-order financial chaotic systems in this paper. There are some main contributions that are worth to be emphasized as follows.(1)Two sufficient conditions are derived for the asymptotical stability of fractional-order financial chaotic systems with fractional orders and , respectively.(2)A linear controller is given to control the fractional-order financial chaotic system.(3)A memoryless nonlinearity function is employed to handle the input saturation problem in fractional-order chaotic systems.

#### 2. Preliminaries and System Description

##### 2.1. Preliminaries

The Caputo definition of fractional-order derivatives can be expressed as [21–24] where represents the fractional order and the Euler function is defined as .

The Laplace transform of Caputo fractional derivative can be given as

The following definition and lemmas will be used.

*Definition 1. *The Mittag-Leffler function with two parameters can be written as
where and , and its Laplace transform can be given as

Lemma 2 (see [22]). *If , is an arbitrary real number, and is a real constant, then
**
where with satisfying .*

Lemma 3 (see [24]). *If and
**
where and all the functions involved are continuous on the interval , then we can obtain
*

*Definition 4 (see [18]). *A memoryless nonlinearity is said to satisfy a sector condition if the following inequality holds:
for constant matrices and , where is a symmetric positive matrix and contains the origin.

Based on the Definition 4, the following lemma holds.

Lemma 5 (see [18]). *Let
**
where with , and
**
then the following inequalities are equivalent:*(1)*;*(2)*;*(3)*.*

Lemma 6. *The autonomous dynamic system
**
is asymptotically stable if the following condition holds:
*

The stability region for is depicted in Figure 1.

##### 2.2. Description of Fractional-Order Financial Chaotic Systems

The fractional-order financial chaotic systems are proposed by [1]. The mathematical model describes a fractional-order financial system including three nonlinear differential equations. The states, , , and , represent the interest rate, the investment demand, and the price index, respectively. The fractional-order model of the system can be described as where denotes the saving amount, is the cost per investment, and is the elasticity of demand of commercial market. is the fractional-order derivative.

#### 3. State-Feedback Controller Design and Stability Analysis

##### 3.1. Fractional Order :

Let us rewrite the controlled system (13) as the following compact form: where , represent the state variables and the control input, respectively. Consider that is the vector-valued saturation function with where represents the symmetric saturation level of the th control input.

Noting that in chaotic systems the states are bounded, the nonlinear function satisfies where is a constant.

Let us define the state-feedback control input as where is the control gain matrix. Then we have where .

Theorem 7. *Consider system (14). If we choose matrices and such that and , then system (14) is asymptotically stable. is a positive constant and will be defined later.*

*Proof. *Taking Laplace transform on (19), we can obtain
where represents the Laplace transform of . Let denote the identity matrix; we have

By taking Laplace inverse transform on (21), we get the solution of system (14):

According to Lemma 2, we know there exist some constants , such that

Then (23) can be rewritten as

From Definition 4 and Lemma 5, we know that satisfies

Let ; we can obtain

By using Lemma 3, we have

Since , from (27) we can conclude that
and this ends the proof.

##### 3.2. Fractional Order :

Let the initial conditions be , . Then we have the following results.

Theorem 8. *Consider system (14). If we choose matrices and such that and , then system (14) is asymptotically stable. is a positive constant.*

*Proof. *Similar to the proof of Theorem 8, taking the Laplace transform and Laplace inverse transform on (14) gives

According to Lemma 2, there exist positive constants , such that

From Definition 4 and Lemma 5, we know that satisfies

Let ; we can obtain

Then we have

Since , from (33) we know that
and this ends the proof.

#### 4. Simulation Studies

The system (14) has 3 equilibriums:

The Jacobian matrix of the fractional-order chaotic system (14), at the equilibrium , can be given as

Let . The eigenvalues for the system equilibrium are . And it is a saddle point. For equilibrium points and they are and . It is a saddle-focus point. Since it is an unstable equilibrium, the condition for chaos is satisfied and the system (14) can show chaotic behavior. We can easily get the minimal commensurate order of the system which is .

*Case 1 (). *Assume the fractional order is . The characteristic equation of the linearized system for the equilibrium is

The characteristic equation of the linearized system for the equilibriums and is
and the unstable eigenvalues are .

Let the initial condition be . The chaotic behavior of uncontrolled fractional-order financial system (13) is shown in Figure 2.

The control gain matrices are chosen as .

Let then we have . From simulation results (see Figure 2), we know . From , we have

From above discussion, we know and . Then we can easily test that the conditions and are satisfied.

Let . The simulation results can be seen in Figures 3 and 4. From the results, we can see that the states variables converge rapidly. The involved system is asymptotic stable. Figure 4 shows the boundedness and smoothness of the saturated control inputs. It can be concluded that good control performance has been achieved.

*Case 2 (). *Let the fractional order be . The chaotic behavior is depicted in Figure 5. In the simulation, the control gain matrices are chosen as ,
then we have

From above discussion, we know and . Then we have and .

Let . The simulation results can be seen in Figures 6 and 7. From the simulation results we can conclude that good control performance has been achieved.

#### 5. Conclusions

We investigate the control problem for fractional-order financial chaotic systems subject to input saturation by means of linear control. Two sufficient conditions are given for the stabilization of such systems with fractional orders and , respectively. A state-feedback controller is designed and the asymptotical stability of the involved system is guaranteed. It is shown that state-feedback controller can be designed to control the fractional-order financial chaotic systems. Simulation studies confirm the results of this paper.

#### Conflict of Interests

The authors do not have a direct financial relation with any commercial identity mentioned in their paper that might lead to a conflict of interests for any of the authors.

#### Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant no. 61001086) and the Fundamental Research Funds for the Central Universities (Grant no. ZYGX2011X004).

#### References

- W. C. Chen, “Nonlinear dynamics and chaos in a fractional-order financial system,”
*Chaos, Solitons and Fractals*, vol. 36, no. 5, pp. 1305–1314, 2008. View at Publisher · View at Google Scholar · View at Scopus - G. Cai and J. Huang, “A new finance chaotic attractor,”
*International Journal of Nonlinear Science*, vol. 3, no. 3, pp. 213–220, 2007. View at Google Scholar · View at MathSciNet - F. Wen and X. Yang, “Skewness of return distribution and coefficient of risk premium,”
*Journal of Systems Science & Complexity*, vol. 22, no. 3, pp. 360–371, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - C. Huang, C. Peng, X. Chen, and F. Wen, “Dynamics analysis of a class of delayed economic model,”
*Abstract and Applied Analysis*, vol. 2013, Article ID 962738, 12 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet - C. Huang, X. Gong, X. Chen, and F. Wen, “Measuring and forecasting volatility in Chinese stock market using HAR-CJ-M model,”
*Abstract and Applied Analysis*, vol. 2013, Article ID 143194, 13 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus - F. Wen and Z. Liu, “A copula-based correlation measure and its application in chinese stock market,”
*International Journal of Information Technology and Decision Making*, vol. 8, no. 4, pp. 787–801, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus - F. Wen, X. Gong, Y. Chao, and X. Chen, “The effects of prior outcomes on risky choice: evidence from the stock market,”
*Mathematical Problems in Engineering*, vol. 2014, Article ID 272518, 8 pages, 2014. View at Publisher · View at Google Scholar · View at MathSciNet - M. Rivero, J. J. Trujillo, L. Vázquez, and M. P. Velasco, “Fractional dynamics of populations,”
*Applied Mathematics and Computation*, vol. 218, no. 3, pp. 1089–1095, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - Z. Wang, X. Huang, and H. Shen, “Control of an uncertain fractional order economic system via adaptive sliding mode,”
*Neurocomputing*, vol. 83, pp. 83–88, 2012. View at Publisher · View at Google Scholar · View at Scopus - J. Cao, D. W. C. Ho, and Y. Yang, “Projective synchronization of a class of delayed chaotic systems via impulsive control,”
*Physics Letters. A*, vol. 373, no. 35, pp. 3128–3133, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus - J. D. Cao and L. L. Li, “Cluster synchronization in an array of hybrid coupled neural networks with delay,”
*Neural Networks*, vol. 22, no. 4, pp. 335–342, 2009. View at Publisher · View at Google Scholar · View at Scopus - J. Cao, A. Alofi, A. Al-Mazrooei, and A. Elaiw, “Synchronization of switched interval networks and applications to chaotic neural networks,”
*Abstract and Applied Analysis*, vol. 2013, Article ID 940573, 11 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet - H. Zhang, J. Cao, and W. Jiang, “Controllability criteria for linear fractional differential systems with state delay and impulses,”
*Journal of Applied Mathematics*, vol. 2013, Article ID 146010, 9 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet - J. D. Cao and Y. Wan, “Matrix measure strategies for stability and synchronization of inertial BAM neural network with time delays,”
*Neural Networks*, vol. 53, pp. 165–172, 2014. View at Google Scholar - M. S. Tavazoei and M. Haeri, “Synchronization of chaotic fractional-order systems via active sliding mode controller,”
*Physica A: Statistical Mechanics and Its Applications*, vol. 387, no. 1, pp. 57–70, 2008. View at Publisher · View at Google Scholar · View at Scopus - M. S. Tavazoei and M. Haeri, “Chaotic attractors in incommensurate fractional order systems,”
*Physica D: Nonlinear Phenomena*, vol. 237, no. 20, pp. 2628–2637, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - Y. F. Xu and Z. M. He, “Synchronization of variable-order fractonal financial systems via active control method,”
*Central European Journal of Physics*, vol. 11, no. 6, pp. 824–835, 2013. View at Google Scholar - Z. Wang, X. Huang, and G. D. Shi, “Analysis of nonlinear dynamics and chaos in a fractional order financial system with time delay,”
*Computers & Mathematics with Applications*, vol. 62, no. 3, pp. 1531–1539, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - Y.-H. Lim, K.-K. Oh, and H.-S. Ahn, “Stability and stabilization of fractional-order linear systems subject to input saturation,”
*IEEE Transactions on Automatic Control*, vol. 58, no. 4, pp. 1062–1067, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - T. Gußner, M. Jost, and J. Adamy, “Controller design for a class of nonlinear systems with input saturation using convex optimization,”
*Systems and Control Letters*, vol. 61, no. 1, pp. 258–265, 2012. View at Publisher · View at Google Scholar · View at Scopus - J. Luo, “State-feedback control for fractional-order nonlinear systems subject to input saturation,”
*Mathematical Problems in Engineering*, vol. 2014, Article ID 891639, 8 pages, 2014. View at Publisher · View at Google Scholar · View at MathSciNet - I. Podlubny,
*Fractional Differential Equations*, vol. 198, Mathematics in Science and Engineering, Academic Press, San Diego, Calif, USA, 1999. - H. S. Ahn and Y. Q. Chen, “Necessary and sufficient stability condition of fractional-order interval linear systems,”
*Automatica*, vol. 44, no. 11, pp. 2985–2988, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus - L. Chen, Y. Chai, R. Wu, and J. Yang, “Stability and stabilization of a class of nonlinear fractional-order systems with caputo derivative,”
*IEEE Transactions on Circuits and Systems II: Express Briefs*, vol. 59, no. 9, pp. 602–606, 2012. View at Publisher · View at Google Scholar · View at Scopus